This summary is organized by the different aspects of the project. Improve access to data: ======================= Access to research data needs to balance scientific requirements with privacy regulations. We explored regulatory rules for accessing data of deceased patients stored in Electronic Health Record. We have proposed in a policy paper how current regulations allow use of deceased patient data and that it may be relatively underused category. We pointed to a problem when a deceased record contains private information on individuals other than the primary patient that is deceased (ref 11). We presented our findings first as a conference submission (ref 1) and later as a paper (ref 2). This project is based on using data from Biomedical Translational Research Information System (BTRIS) (ref 3) Data organization: ======================= We explored the best way to organize and maintain large Integrated Data Repositories (IDRs). We have formulated a list of requirements and evaluated how they are fulfilled by existing architectures (ref 4) Clinical research enterprise: ======================= Within NIH IRP, we explored how clinical trial stored in registries can be linked with published data in PubMed. Based on these findings, we have explored the whole clinical trial registry for years 2004-2009 and linkage to PubMed for all registered trials and all articles in PubMed (ref 5). Based on these findings, we have also published where this linkage is missing. Correct association of a result article with a registered clinical trial is dependent on manuscript writers correctly referencing the relevant clinical trial per policy of ICMJE. We published an evaluation of adherence to this policy (ref 6). Clinical Bioinformatics: ======================= BTRIS contains whole exome sequencing data and this can be used to generate drug dosage requirements. We explored how dosing guidelines from CPIC can be executed against whole exome data (ref 7). In addition to implementation, we participate on efforts to improve future dosing guidelines (ref 8). We have also analyzed the current databases and knowledge bases that support genomic and personalized medicine (ref 10) Knowledge representation: ======================= As part of a general effort to represent knowledge in database and ontology systems, we participate on efforts to produce large scale knowledge bases for pharmacovigilance (ref 9).